Knowledge catalogue
Conservative Judge J. Michael Luttig is warning that Trump is already preparing to rig the 2026 midterms, not by one single scheme but by a whole “buffet of options” to throw the election into chaos.
arXiv:2601.06394v3 Announce Type: replace Abstract: Understanding student behavior in the classroom is essential to improve both pedagogical quality and student engagement. Existing methods for predic
arXiv:2604.16580v1 Announce Type: new Abstract: Accurate assessment of lithium-ion battery ageing is challenged by cell-to-cell variability, heterogeneous cycling protocols, and limited transferabilit
arXiv:2604.18197v1 Announce Type: cross Abstract: Qualitative methods are important to use alongside quantitative methods to improve Human-Robot Interaction (HRI), yet they are often applied in static
arXiv:2604.17761v1 Announce Type: cross Abstract: Interpretability tools are increasingly used to analyze failures of Large Language Models (LLMs), yet prior work largely focuses on short prompts or t
arXiv:2604.16975v1 Announce Type: cross Abstract: Recent work has shown that parameterizing and optimizing coordinate transformations using normalizing flows, i.e., invertible neural networks, can sig
arXiv:2603.07084v2 Announce Type: replace-cross Abstract: Reward hacking is a form of misalignment in which models overoptimize proxy rewards without genuinely solving the underlying task. Precisely m
arXiv:2604.16363v1 Announce Type: cross Abstract: Text-to-image models are commercially valuable assets often distributed under restrictive licenses, but such licenses are enforceable only when violat
arXiv:2507.21934v2 Announce Type: replace Abstract: In cross-cultural recipe adaptation, the goal is not only to ensure cultural appropriateness and retain the original dish's essence, but also to pro
arXiv:2509.03070v4 Announce Type: replace-cross Abstract: This letter presents a CWT-enhanced vibration sensing framework for bearing fault monitoring through spatial localization on time-frequency sp
arXiv:2604.17082v1 Announce Type: new Abstract: Capturing both geometry and rigid motion for structured dynamic objects, like multi-part assemblies or jointed mechanisms, remains a key challenge. Exis
arXiv:2604.16684v1 Announce Type: new Abstract: We study model-free reinforcement learning (RL) in non-stationary finite-horizon episodic Markov decision processes (MDPs) without prior knowledge of th
arXiv:2604.17833v1 Announce Type: new Abstract: What appears effortless to a human waiter remains a major challenge for robots. Manipulating objects nonprehensilely on a tray is inherently difficult,
arXiv:2507.06056v4 Announce Type: replace Abstract: Large Language Models (LLMs) are known to memorize portions of their training data, sometimes even reproduce content verbatim when prompted appropri
arXiv:2604.16380v1 Announce Type: new Abstract: Large language models (LLMs) rely on pretraining on massive and heterogeneous corpora, where training data composition has a decisive impact on training
arXiv:2604.18122v1 Announce Type: new Abstract: Decision-making is a cognitively intensive task that requires synthesizing relevant information from multiple unstructured sources, weighing competing f
arXiv:2604.17177v1 Announce Type: new Abstract: Fine-tuning adapts pretrained networks to new objectives. Whether the resulting depth profile of representational change reflects an intrinsic property
arXiv:2510.17422v4 Announce Type: replace Abstract: Keypoint detection is the foundation of many computer vision tasks, including image registration, structure-from-motion, 3D reconstruction, visual o
arXiv:2604.18261v1 Announce Type: cross Abstract: The multi-scale and non-linear nature of phase-field models of solidification requires fine spatial and temporal discretization, leading to long compu
arXiv:2604.16656v1 Announce Type: new Abstract: All languages are equal; when it comes to tokenization, some are more equal than others. Tokens are the hidden currency that dictate the cost and latenc
arXiv:2604.17709v1 Announce Type: new Abstract: Existing works on large language model (LLM) decomposition mainly focus on improving performance on downstream tasks, but they ignore the poor parallel
arXiv:2510.19544v2 Announce Type: replace-cross Abstract: Combinatorial optimization problems are central to both practical applications and the development of optimization methods. While classical an
arXiv:2604.17286v1 Announce Type: new Abstract: Visual Autoregressive (VAR) modeling inefficiently applies a fixed computational depth to each position when generating high-resolution images. While ex
arXiv:2604.17585v1 Announce Type: new Abstract: Salient object detection (SOD) requires modeling both long-range contextual dependencies and fine-grained structural details, which remains challenging
arXiv:2604.18201v1 Announce Type: new Abstract: Diffusion models have emerged as powerful tools for a wide range of vision tasks, including text-guided image generation and editing. In this work, we e
arXiv:2604.16670v1 Announce Type: new Abstract: We present a framework leveraging a novel variant of the model-based diffusion algorithm to minimize the time required for a redundant dual-arm robot co
In the past, even with an icebreaker and during peak melt season, getting to the North Pole wasn’t a sure bet. It took favorable winds to crack the frozen ocean surface, and ships had to fight through
arXiv:2604.16431v1 Announce Type: new Abstract: Abrupt transitions between distinct dynamical regimes are a hallmark of complex systems. Grokking in deep neural networks provides a striking example --
arXiv:2602.05449v3 Announce Type: replace Abstract: While diffusion models have achieved great success in the field of video generation, this progress is accompanied by a rapidly escalating computatio
arXiv:2512.10906v2 Announce Type: replace-cross Abstract: We consider a class of finite-horizon, linear-quadratic stochastic control problems, where the probability distribution governing the noise pr
arXiv:2601.03066v2 Announce Type: replace Abstract: Large language models solve complex tasks by generating long reasoning chains, achieving higher accuracy at the cost of increased computational cost
arXiv:2603.11024v2 Announce Type: replace Abstract: VLMs have become increasingly proficient at a range of computer vision tasks, such as visual question answering and object detection. This includes
arXiv:2604.18256v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) models provide a structured approach to combining specialized neural networks and offer greater interpretability than conventio
arXiv:2604.18368v1 Announce Type: new Abstract: A key challenge in segmentation in digital histopathology is inter- and intra-stain variations as it reduces model performance. Labelling each stain is
arXiv:2602.10764v2 Announce Type: replace Abstract: The slow iterative sampling nature remains a major bottleneck for the practical deployment of diffusion and flow-based generative models. While cons
arXiv:2604.17542v1 Announce Type: new Abstract: Conventional test-time adaptation (TTA) approaches typically adapt the model using only a small fraction of test samples, often those with low-entropy p
arXiv:2604.18540v1 Announce Type: cross Abstract: Adversarial training of binary classifiers can be reformulated as regularized risk minimization involving a nonlocal total variation. Building on this
arXiv:2503.14324v3 Announce Type: replace-cross Abstract: The differing representation spaces required for visual understanding and generation pose a challenge in unifying them within the autoregressi
arXiv:2604.16483v1 Announce Type: new Abstract: Concept erasure in Text-To-Image (T2I) diffusion models is vital for safe content generation, but existing inference-time methods face significant limit
arXiv:2604.18080v1 Announce Type: cross Abstract: While attack graphs are useful for identifying major cybersecurity threats affecting a system, they do not provide operational support for determining
arXiv:2604.17319v1 Announce Type: cross Abstract: Grounded Multimodal Named Entity Recognition (GMNER) aims to jointly identify named entity mentions in text, predict their semantic types, and ground
arXiv:2604.17047v1 Announce Type: cross Abstract: We present E2E-WAVE, the first end-to-end learned waveform generation system for underwater video multicasting. Acoustic channels exhibit 20--46% bit
arXiv:2511.10834v3 Announce Type: replace Abstract: Low-latency delivery of satellite imagery is essential for time-critical applications such as disaster response, intelligence, and infrastructure mo
arXiv:2604.18367v1 Announce Type: new Abstract: Early action prediction seeks to anticipate an action before it fully unfolds, but limited visual evidence makes this task especially challenging. We in
arXiv:2602.03986v2 Announce Type: replace Abstract: Conformal prediction, a post-hoc, distribution-free, finite-sample method of uncertainty quantification that offers formal coverage guarantees under
arXiv:2604.16783v1 Announce Type: new Abstract: Vehicle trajectory prediction is central to highway perception, but deployment on roadside edge devices necessitates bounded, deterministic end-to-end l
arXiv:2604.18220v1 Announce Type: cross Abstract: Electroencephalography (EEG) signals have been promising for long-term braking intensity prediction but are prone to various artifacts that limit thei
arXiv:2510.12916v2 Announce Type: replace-cross Abstract: Systems of interacting continuous-time Markov chains are a powerful model class, but inference is typically intractable in high dimensional se
arXiv:2604.18106v1 Announce Type: new Abstract: Adapting large language models (LLMs) to low-resource languages (LRLs) is constrained by the scarcity of task data and computational resources. Although
arXiv:2604.17749v1 Announce Type: new Abstract: Understanding physical transformation processes is crucial for both human cognition and artificial intelligence systems, particularly from an egocentric
arXiv:2601.09825v2 Announce Type: replace Abstract: We establish a lower bound on the eluder dimension of generalised linear model classes, showing that standard eluder dimension-based analysis cannot
arXiv:2604.17211v1 Announce Type: new Abstract: We present EmbodiedHead, a speech-driven talking-head framework that equips LLMs with real-time visual avatars for conversation. A practical embodied av
arXiv:2602.07794v3 Announce Type: replace Abstract: Large language models (LLMs) exhibit emergent behaviors suggestive of human-like reasoning. While recent work has identified structured conceptual r
arXiv:2510.23969v2 Announce Type: replace-cross Abstract: We present a neuromuscular speech interface that translates electromyographic (EMG) signals recorded from orofacial muscles during speech arti
arXiv:2602.16161v3 Announce Type: replace-cross Abstract: Emotional expression underpins natural communication and effective human-computer interaction. We present Emotion Collider (EC-Net), a hyperbo
arXiv:2511.12554v2 Announce Type: replace Abstract: Visual Emotion Analysis (VEA) aims to bridge the affective gap between visual content and human emotional responses. Despite its promise, progress i
En 2021, les opposants à la vaccination craignaient 'une hécatombe' dans les années à venir. Nous sommes 5 ans plus tard et une immense étude sur les données de 29 millions de Français (23 millions va
arXiv:2601.05508v2 Announce Type: replace-cross Abstract: Hieroglyphs, as logographic writing systems, encode rich semantic and cultural information within their internal structural composition. Yet,
arXiv:2604.18066v1 Announce Type: cross Abstract: Anomaly-based Intrusion Detection Systems (IDSs) ensure protection against malicious attacks on networked systems. While deep learning-based IDSs achi
arXiv:2603.15299v2 Announce Type: replace Abstract: We propose a novel approach which exploits chaos to enhance classification accuracy. Specifically, the available data that need to be classified are